Voice Search and AI Search: How to Adapt Your Site for Both
Voice and AI search aren't a future trend — they're the current state of search results: Google's AI Overviews now show for a significant share of informational queries, and ChatGPT, Perplexity, and Gemini generate their own traffic sources. Here's what to actually change on your site so AI systems find you, understand you, and cite you.
How AI search differs from classic SEO
In classic search, the goal is a top-10 link. In AI search, the goal is landing inside the citation or answer the model generates — and the user may never click through if the AI interface already gives a complete answer. That changes the success metric: it's not just clicks, but brand mentions inside AI-generated answers.
AI systems work differently under the hood: Google AI Overviews is generated from Google's own index and functions similarly to featured snippets, but synthesizes across multiple sources. Search-enabled ChatGPT, Perplexity, and Claude use their own crawlers and live web retrieval, so being indexed by them depends on your site being accessible to their specific bots, not just Googlebot.
llms.txt: a new file for a new audience
llms.txt is a proposed (not yet universally supported) standard placed at the site root, similar to robots.txt, that describes site structure and key pages in plain markdown for LLM agents, saving them the token cost of crawling the whole site themselves.
Important context: this isn't a guaranteed ranking signal the way robots.txt governs crawling — it's a recommendation different AI systems support to varying degrees. Still, adding the file is cheap, breaks nothing, and potentially helps, so it's worth treating as baseline technical hygiene.
Structure is simple: an H1 with the site name, a short description in a blockquote, then sections linking to key pages — services, docs, contact — each with a one-line description.
AI crawler access: robots.txt and HTTP headers
Check robots.txt for explicit blocks on GPTBot (OpenAI), ClaudeBot (Anthropic), PerplexityBot, and Google-Extended — that last directive separately governs whether content is used for training and Gemini's generated answers, distinct from regular Googlebot. Blocking these bots is technically possible and sometimes justified for business reasons, but it also excludes the site from AI citation entirely.
Separately check whether a CDN or WAF is silently blocking traffic by User-Agent — some default security configurations block unrecognized bots by default, including legitimate AI crawlers, without the site owner even knowing.
Structured data for AI answers
Schema.org markup (JSON-LD) remains a key signal for both classic and AI search: FAQPage, HowTo, and Article types with author and datePublished fields, Organization with sameAs links to social profiles — all help AI systems extract facts precisely instead of interpreting unstructured prose.
FAQPage markup matters especially for voice search: a voice assistant physically speaks one direct answer, and a structured question-answer pair is the most reliable source for that extraction compared to a paragraph of prose where the answer is diffused among context.
Content AI systems actually want to cite
Answer-first structure — the first paragraph gives a direct, self-contained answer to the question in the heading, with no preamble. AI systems extract exactly that chunk of text as a citation because it's grammatically and logically complete on its own.
Specific numbers, dates, and names outperform vague phrasing: 'registering a sole proprietorship in Uzbekistan takes 3 business days via the my.gov.uz portal' gets cited more readily than 'registration takes a little time.' AI models favor verifiable facts with clear source attribution and dates.
Conversational long-tail queries — voice and AI search are increasingly phrased as natural questions ('which dental clinic in Tashkent is open on weekends') rather than short keyword phrases. Content answering full questions in H2 headings covers both query formats.
E-E-A-T as a citation gate, not just a ranking factor
AI systems are objectively more cautious about trust than classic search — Google and OpenAI publicly discuss reducing misinformation risk in generative answers. A named author with a professional bio, links to primary sources, and update dates all raise the odds that content gets pulled into the citable set instead of filtered out as unreliable.
A practical step: add an 'about the author' block with credentials — especially on medical, legal, and financial pages that fall into the YMYL category — plus a link to a LinkedIn or industry registry profile. This works simultaneously for Google's E-E-A-T and for AI model trust signals.